Apparatus, methods, and articles of manufacture to predict vectored digital subscriber line (DSL) performance gains

ABSTRACT

Apparatus, methods and articles of manufacture to predict vectored digital subscriber line (DSL) performance gains are disclosed. A disclosed example method includes determining a model coefficient of a noise-to-margin ratio (NMR) model from performance data measured for a DSL subscriber loop prior to provisioning of vectoring for the DSL subscriber loop, computing, using the model coefficient, a first NMR value with disturbers enabled and a second NMR value with disturbers disabled, and estimating an expected vectoring performance gain for the DSL subscriber loop based on the first and second NMR values.

FIELD OF THE DISCLOSURE

This disclosure relates generally to digital subscriber line (DSL)subscriber loops and, more particularly, to apparatus, methods andarticles of manufacture to predict vectored DSL performance gains forsubscriber loops.

BACKGROUND

Vectored DSL is an emerging DSL technology that offers significantimprovements in achievable transmission rates over copper twisted-pairwires. In vectored DSL communication systems, a vectoring engine usescollected measurements characterizing crosstalk between a set ofsubscriber loops to reduce the effects of the crosstalk experienced bythose subscriber loops.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic illustration of an example DSL communicationsystem constructed in accordance with the teachings of this disclosure.

FIG. 2 illustrates an example manner of implementing the example dataanalysis system of FIG. 1.

FIG. 3 is a flowchart representative of example machine-accessibleinstructions that may be executed by, for example, one or moreprocessors to implement the example data analysis systems of FIGS. 1 and2.

FIG. 4 is a schematic illustration of an example processor platform thatmay be used and/or programmed to execute the example machine accessibleinstructions of FIG. 3 to implement the example systems of FIGS. 1and/or 2.

DETAILED DESCRIPTION

Example apparatus, methods, and articles of manufacture to predictvectored DSL performance gains are disclosed. Installation of vectoreddigital subscriber line (DSL) communication systems may introduceoperational, administrative and/or maintenance problems. For example,for cost efficiency it may be desirable to install vectoring technologywhen the vectoring technology can achieve a desired outcome such as adesired return-on-investment. In other words, there may be a tradeoffbetween expected circuit performance gains and the cost of deployingvectoring technology to achieve those expected performance gains. Thus,there may be a benefit to estimating the performance gains thatvectoring can achieve prior to deployment of vectoring technology.Examples disclosed herein enable a network operator to quantify theperformance gain(s) that are expected to result if vectoring weredeployed on particular subscriber loops in their network(s).

A disclosed example method includes determining a model coefficient of anoise-to-margin ratio (NMR) model from performance data measured for aDSL subscriber loop prior to provisioning of vectoring for the DSLsubscriber loop. The method computes, using the model coefficient, afirst NMR value with disturbers enabled and a second NMR value withdisturbers disabled. The method estimates an expected vectoringperformance gain for the DSL subscriber loop based on the first andsecond NMR values.

A disclosed example apparatus includes a modeler and a performance gainestimator. The modeler determines a model coefficient using a leastsquares analysis of performance data measured for a DSL subscriber loopprior to provisioning of vectoring for the DSL subscriber loop. Theperformance gain estimator computes, using the model coefficient, afirst NMR value with disturbers enabled and a second NMR value withdisturbers disabled, and estimates an expected vectoring performancegain for the DSL subscriber loop based on the first and second NMRvalues.

FIG. 1 illustrates an example DSL communication system 100. The exampleDSL communication system 100 of FIG. 1 includes any number and/ortype(s) of central offices (COs) 105, remote terminals (RTs) and/orserving area interfaces (SAIs). The example CO 105 of FIG. 1, other COs,RTs and/or SAIs may be used to provide data and/or communicationservices to one or more customer premises, two of which are designatedat reference numerals 110 and 111. Example data and/or communicationservices include, but are not limited to, telephone services, Internetservices, data services, messaging services, instant messaging services,electronic mail (email) services, chat services, video services, audioservices, and gaming services.

To provide communication services to the customer premises 110, 111, theexample CO 105 of FIG. 1 includes any number and/or type(s) of DSLaccess multiplexer(s) (DSLAM(s)) 115 and/or vectored video-ready accessdevice(s) (VRAD(s)), and the example customer premises 110, 111 of FIG.1 each include customer-premises equipment (CPE) DSL modems 120 and 121.The example vectored DSLAM 115 of FIG. 1 includes and/or implements CODSL modems (not shown) for respective ones of the customer premises 110,111. CO DSL modems are sometimes referred to as “DSLAM ports,” “VRADports,” or simply “ports.” In the illustrated example of FIG. 1, CO DSLmodems are implemented on DSLAM linecards 125, 126, which each implementa plurality of CO DSL modems for respective ones of a plurality of CPEDSL modems (e.g., the example CPE DSL modems 120, 121). In theillustrated example, each of linecards 125, 126 implements sixty-four CODSL modems.

The example DSLAM 115, the CO DSL modems within the DSLAM 115, and/orthe example CPE DSL modems 120, 121 of FIG. 1 may be implemented, forexample, in accordance with the International TelecommunicationsUnion—Telecommunications Sector (ITU-T) G.993.x family of standards forVDSL, and/or the ITU-T G.992.x family of standards for asymmetric DSL(ADSL). However, the CO DSL modems and/or the CPE DSL modems 120, 121may additionally or alternatively be implemented in accordance with anypast, present and/or future standard, specification and/orrecommendation relating to DSL or its associated technologies.

While in the illustrated example of FIG. 1, the DSLAM 115 is implementedat the CO 105, the DSLAM 115, another DSLAM and/or a VRAD mayadditionally or alternatively be implemented at an RT, at an SAI and/orat any other location between the CO 105 and the customer premises 110,111. In such instances, a fiber-optic cable (not shown) may be used, forexample, to communicatively couple the remotely located DSLAM/VRAD tothe CO 105.

In the illustrated example of FIG. 1, the DSLAM 115 provides DSLservices to the CPE DSL modems 120, 121 via respective subscriber loops130 and 131. Subscriber loops are sometimes referred to as “wire-pairs,”“telephone lines,” “subscriber lines” and/or “loops.” While throughoutthis disclosure reference is made to the example subscriber loops 130,131 of FIG. 1, a subscriber loop (e.g., any of the example subscriberloops 130, 131) used to provide a DSL service to a customer premises(e.g., any of the customer premises 110, 111) may include and/or beconstructed from one or more segments of copper twisted-pair wire (e.g.,any combination of a feeder one (F1) cable, a feeder two (F2) cable, afeeder three (F3) cable, a feeder four (F4) cable, a distribution cable,a drop cable, and/or customer-premises wiring), terminals, and/ordistributions points (e.g., an RT, an SAI, a serving terminal, a vault,a pedestal and/or any other type(s) of wiring distribution points). Suchsegments of copper twisted-pair wire may be spliced and/or connectedend-to-end, and/or may be connected at only one end, thereby creatingone or more bridged-taps. Regardless of the number, type(s), gauge(s)and/or topology of copper twisted-pair wires used to construct theexample subscriber loops 130, 131, they will be referred to herein inthe singular form. Nevertheless, it is to be understood that the term“subscriber loop” may refer to one or more copper twisted-pair wiresegments and may or may not include one or more bridged taps.

When two subscriber loops (e.g., the example subscriber loops 130, 131)are part of the same cable bundle or cable binder 135, the signaltransmissions on one subscriber loop (for example, on the subscriberloop 130) may cause or introduce crosstalk into other subscriber loopsof the same binder 135 (for example, onto the subscriber loop 131). Theamount of crosstalk varies with, for example, signal transmission level,frequency and/or length of the binder 135. Crosstalk may interfere withand/or reduce the achievable transmission speed on a particularsubscriber loop 130, 131. As used herein, the term “disturber” refers toa DSL service on a particular subscriber loop that introduces crosstalkinto another subscriber loop. As used herein, a disturber is enabledwhen the DSL service on its associated subscriber loop is active orin-service and, thus, is currently introducing crosstalk into anothersubscriber loop. As used herein, a disturber is disabled when the DSLservice on its associated subscriber loop is inactive or out-of-serviceand, thus, is not currently introducing crosstalk into anothersubscriber loop. In the illustrated example, the binder 135 includestwenty-five copper twisted-pair wires.

In some examples, a 25-pair cable binder is divided into groupings of 5wire pairs (e.g., pairs 1-5, 6-10, etc.). In some examples, the 25-pairbinder groups are combined into groups of 4. A group of 4 25-pairbinders may be referred to as a hundred-pair count or a complement, witha first binder (binder1) containing pairs 1-25, a second binder (binder2) containing pairs 26-50, etc. In some examples, a distribution areacable includes multiple complements (e.g., complement 1 contains binders1-4, complement 2 contains binders 5-8, etc.). During manufacture,color-coded plastic bands may be placed around binders and bindercomplements to identify the corresponding binder and complement. Thiscolor coding facilitates use and re-use of wire pair colors in thefield. In some examples, wire pairs in binders and complements are wovenaround each other but not woven together during manufacture. The coloredplastic band wraps help ensure that a binder group remains intact as faras possible into the distribution area, with the pairs keeping theirphysical co-extensiveness as long as is physically possible.

To collect performance data, the example DSL communication system 100 ofFIG. 1 includes an element management system 140. The example elementmanagement system 140 of FIG. 1 periodically (e.g., weekly, daily,hourly, etc.) or aperiodically collects performance data (e.g., amaximum attainable data rate, an error count, an estimated loop length,a DSL connection rate, a loop attenuation value, an error rate, asignal-to-noise ratio, a bit allocation, a noise margin, a DSL modemconfiguration, etc.) from the example DSLAM 115 and/or the examplecustomer-premises DSL modems 120, 121.

To maintain topological data, the example DSL communication system 100of FIG. 1 includes an outside plant records system 145. The exampleoutside plant records system 145 of FIG. 1 maintains, stores and/ormakes accessible an inventory of the equipment and/or theinterconnections of the equipment of the example DSL communicationsystem 100 including, but not limited to, assignments of the subscriberloops 130, 131 to DSLAM ports. In some examples, the example outsideplant records system 145 maintains, stores and/or records for eachsubscriber loop 130, 131 a 4-tuple representing a wire center, adistribution area, a cable 135, and a wire pair.

To proactively monitor the performance of the DSL communication system100, the example DSL communication system 100 of FIG. 1 includes a dataacquisition system 150. Based on a schedule (e.g., hourly, daily,weekly, etc.) and/or when initiated by, for example, a technician and/ormaintenance personnel, the example data acquisition system 150 of FIG. 1collects attributes such as performance data and configurationparameters via the element management system 140 and stores thecollected performance data in an aggregate database 155. Duringcollection, the example data acquisition system 150 associates thecollected attributes for a particular subscriber loop 130, 131 with acustomer billing access number (BAN) assigned to that subscriber loop130, 131. Example attributes that may be collected include, but are notlimited to, a quiet line noise (QLN), a current bit rate, a plurality ofattenuations for respective ones of a plurality of frequencies (i.e., anattenuation vector), and a status of the line. An example statusattribute is a flag having a first value when the associated subscriberloop is out-of-service and a second value when the loop is in service oractive.

To store performance data and topological information, the example DSLcommunication system 100 of FIG. 1 includes the example aggregatedatabase 155. The example aggregate database 155 of FIG. 1 storesperformance data and outside plant topology information for a pluralityof subscriber loops (e.g., the example subscriber loops 130, 131). Theperformance data and/or outside topology information is stored togetherwith a BAN that identifies (in some examples, uniquely) the associatedsubscriber loop 130, 131, and a timestamp representing when, forexample, the performance data was measured. Any number and/or type(s) ofdata structures may be used to implement the example aggregate database155 of FIG. 1. The example aggregate database 155 may be implemented byany number and/or type(s) of volatile and/or non-volatile memory(ies),memory device(s), and/or storage device(s).

To extract topological information, the example DSL communication system100 of FIG. 1 includes a loop information acquisition system 160.Aperiodically or periodically (e.g., daily), the example loopinformation acquisition system 160 of FIG. 1 extracts topologyinformation from the example outside plant records system 145 and storesthe extracted topology information in the example aggregate database155. While collecting topology information, the example loop informationacquisition system 160 associates the topological information for aparticular subscriber loop with the BAN assigned to that subscriber loopand a timestamp representing, for example, when the topologicalinformation was extracted from the outside plant records system 145.Using any number and/or type(s) of method(s), message(s), protocol(s)and/or script(s), the example loop information acquisition system 160 ofFIG. 1 extracts topology information from the example outside plantrecords system 145 and produces one or more output reports that areparsed and used to obtain the topology information that is stored in theexample aggregate database 155.

To estimate vectored DSL performance gains, the example DSLcommunication system 100 of FIG. 1 includes a data analysis system 165.As explained in more detail below in connection with FIGS. 2-4, theexample data analysis system 165 of FIG. 1 obtains performance data andtopology information from the example aggregate database 155, and usesthe obtained data and information to construct an NMR estimator model.The example data analysis system 165 uses the NMR estimator model toestimate a first NMR value with crosstalk disturbers disabled and asecond NMR value with the crosstalk disturbers enabled. The dataanalysis system 165 estimates a vectored DSL performance gain bycomputing a difference between the estimated first and second NMRvalues. An example manner of implementing the example data analysissystem 165 of FIG. 1 is described below in connection with FIG. 2.

While an example DSL communication system 100 is illustrated in FIG. 1,one or more of the elements, processes and/or devices illustrated inFIG. 1 may be combined, divided, re-arranged, omitted, eliminated and/orimplemented in any other way. Further, the example element managementsystem 140, the example outside plant records system 145, the exampledata acquisition system 150, the example aggregate database 155, theexample loop information acquisition system 160 and/or the example dataanalysis system 165 may be implemented by hardware, software, firmwareand/or any combination of hardware, software and/or firmware. Thus, forexample, any of the example element management system 140, the exampleoutside plant records system 145, the example data acquisition system150, the example aggregate database 155, the example loop informationacquisition system 160 and/or the example data analysis system 165 couldbe implemented by the example processor platform P100 of FIG. 6 and/orone or more circuit(s), programmable processor(s), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)), field-programmablegate array(s) (FPGA(s)), fuses, etc. When any apparatus claim of thispatent incorporating one or more of these elements is read to cover apurely software and/or firmware implementation, at least one of theexample element management system 140, the example outside plant recordssystem 145, the example data acquisition system 150, the exampleaggregate database 155, the example loop information acquisition system160 and/or the example data analysis system 165 are hereby expresslydefined to include a tangible article of manufacture such as a tangiblecomputer-readable medium storing the firmware and/or software. Furtherstill, the example DSL communication system 100 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 1, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

As used herein, the term tangible computer-readable medium is expresslydefined to include any type of computer-readable medium and to expresslyexclude propagating signals. Example computer-readable medium include,but are not limited to, a volatile and/or non-volatile memory, avolatile and/or non-volatile memory device, a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a read-only memory (ROM), arandom-access memory (RAM), a programmable ROM (PROM), anelectronically-programmable ROM (EPROM), an electronically-erasable PROM(EEPROM), an optical storage disk, an optical storage device, magneticstorage disk, a magnetic storage device, a cache, and/or any otherstorage media in which information is stored for any duration (e.g., forextended time periods, permanently, brief instances, for temporarilybuffering, and/or for caching of the information) and which can beaccessed by a processor, a computer and/or other machine having aprocessor, such as the example processor platform P100 discussed belowin connection with FIG. 6. As used herein, the term non-transitorycomputer-readable medium is expressly defined to include any type ofcomputer-readable medium and to exclude propagating signals.

FIG. 2 illustrates an example manner of implementing the example dataanalysis system 165 of FIG. 1. To communicate with the example aggregatedatabase 155, the example data analysis system 165 of FIG. 2 includesany number and/or type(s) of database interface(s) 205. Using any numberand/or type(s) of message(s), protocol(s), interface(s), method(s),logic and/or application programming interface(s), the example databaseinterface 205 of FIG. 2 enables a data manager 210 to obtain performanceand loop topology information from the example aggregate database 155.

To generate data suitable for defining an NMR model, the example dataanalysis system 165 of FIG. 2 includes the example data manager 210. Forexample and with reference to a subscriber loop (e.g., one of theexample subscriber loops 130, 131 of FIG. 1), the data manager 210 ofFIG. 2 obtains an attenuation vector, and estimates an equivalentworking length (EWL) for the subscriber loop 130, 131 from theattenuation vector. In some examples, the EWL is expressed in units of26 gauge copper wire. The example data manager 210 may estimate the EWLby, for example, curve fitting the attenuation values of the obtainedattenuation vector to an attenuation versus frequency equation for 26gauge copper wire. When bridge taps are detected, the example datamanager 210 may correct for the presence of the bridge taps whenperforming the curve fitting.

The example data manager 210 of FIG. 2 also obtains from the aggregatedatabase 155, via the database interface 205, one or more NMR valuespreviously measured for the subscriber loop under analysis (e.g., one ofthe subscriber loops 130, 131). For each collected NMR value, the datamanager 210 uses the timestamp associated with a currently consideredNMR value to obtain associated topology information. The data manager210 uses the timestamp to identify topology information applicable tothe time period during which the currently considered NMR value wasmeasured. The data manager 210 uses the identified topology informationto identify other subscriber loops belonging to the same cable binder,complement and/or distribution area as the presently consideredsubscriber loop (e.g., one of the subscriber loops 130, 131). For eachof the identified subscriber loops, the data manager 210 uses thetimestamp to determine whether the subscriber loop was out-of-service orin-service during the time period associated with the currentlyconsidered NMR. In other words, for each NMR value, the example datamanager 210 determines the number of active subscriber loops that mayhave contributed crosstalk (i.e., a disturber count) during the timeperiod that the NMR value was measured. In some examples, a subscriberloop is only included in the disturber count when a DSL service is inservice on the subscriber loop.

For a presently considered subscriber loop “i”, the example data manager210 of FIG. 2 computes four disturber terms M(i,25pair), M(i,other75),M(i,other_cable) and M(i,other_DA), where M(i,25pair) represents thenumber of active DSL circuits in the same binder as “i”, M(i,other75)represents the number of active DSL circuits in the same complement butnot in the same binder, M(i, other_cable) represents the number ofactive DSL pairs in the same cable but not in the same complement, andM(i,other_DA) represents the number of active DSL circuits in thedistribution area but not inside the cable as “i”. The phrase “other 75”means the other 75 pairs in the 100 pair complement. The phase “othercable” means others in the cable but not in the same complement as pairi. The example disturber terms M(i,25pair), M(i,other75),M(i,other_cable) and M(i,other_DA) are mutually exclusive counts and arenot arithmetically dependent counts to reduce the stochastic dependencebetween interferer counts of binder group cable.

In some examples, the example data manager 210 of FIG. 2 stores thecomputed EWL estimate and disturber counts in the example aggregatedatabase 155 for subsequent retrieval during, for example, capacityand/or engineering planning relating to the deployment of vectored DSLtechnologies.

To determine an NMR model, the example data analysis system 165 of FIG.2 includes a modeler 215. The example modeler 215 of FIG. 2 uses theobtained NMR values, the estimated EWL, and the computed disturbercounts to estimate the coefficients of the NMR model. In some examples,the modeler 215 uses a least squares regression to determine thecoefficients of the model. An example NMR model can be expressedmathematically as:

$\begin{matrix}{{NMR}_{i} = {\alpha + {\beta_{1}*{\log\left( {M_{i,{25{pait}}} + 1} \right)}} + {\beta_{2}*{\log\left( {M_{i,{{other}\; 75}} + 1} \right)}} + {\beta_{3}*{\log\left( {M_{i,{other\_ cable}} + 1} \right)}} + {\beta_{4}*{\log\left( {M_{i,{other\_ DA}} + 1} \right)}} + {\beta_{5}*{EWL}} + ɛ_{i}}} & {{EQN}\mspace{14mu}(1)}\end{matrix}$Where log( ) represents a logarithm base 10, i represents the presentlyconsidered loop, α is a y-intercept, β_(i) are the model coefficients tobe selected or determined, and ε_(i) is an error term. The examplemodeler 215 selects the model coefficients β_(i) to reduce (e.g.,minimize) the error term ε_(i). In some examples, the modeler 215 ofFIG. 2 stores the model coefficients in the example aggregate database155 for subsequent retrieval during, for example, capacity and/orengineering planning relating to the deployment of vectored DSLtechnologies.

To estimate vectored DSL performance gains, the example data analysissystem 165 of FIG. 2 includes a performance gain estimator 220. Theexample performance gain estimator 220 of FIG. 2 uses EQN (1) to computea first NMR value assuming disturbers are enabled and a second NMR valueassuming the disturbers are disabled. The example performance gainestimator 220 computes a difference between the first and second NMRvalues. That difference represents an estimate of the performance gainachievable if vectoring were provisioned for the subscriber loop i. Insome examples, the example performance gain estimator 220 of FIG. 2stores the estimated vectored DSL performance gain in the exampleaggregate database 155 for subsequent retrieval during, for example,capacity and/or engineering planning relating to the deployment ofvectored DSL technologies.

While a manner of implementing the example data analysis system 165 ofFIG. 1 is illustrated in FIG. 2, one or more of the elements, processesand/or devices illustrated in FIG. 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example database interface 205, the example data manager210, the example modeler 215, the example performance gain estimator 220and/or, more generally, the example data analysis system 165 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample database interface 205, the example data manager 210, theexample modeler 215, the example performance gain estimator 220 and/or,more generally, the example data analysis system 165 could beimplemented by the example processor platform P100 of FIG. 4 and/or oneor more circuit(s), programmable processor(s), application specificintegrated circuit(s) ASIC(s), PLD(s) and/or FPLD(s), FPGA(s), fuses,etc. When any apparatus claim of this patent incorporating one or moreof these elements is read to cover a purely software and/or firmwareimplementation, at least one of the example database interface 205, theexample data manager 210, the example modeler 215, the exampleperformance gain estimator 220 and/or, more generally, the example dataanalysis system 165 are hereby expressly defined to include a tangiblearticle of manufacture such as a tangible computer-readable mediumstoring the firmware and/or software. Further still, the example dataanalysis system 165 may include one or more elements, processes and/ordevices in addition to, or instead of, those illustrated in FIG. 2,and/or may include more than one of any or all of the illustratedelements, processes and devices.

FIG. 3 is a flowchart representing machine-accessible instructions thatmay be executed by, for example, one or more processors to predictvectored DSL performance gains. A processor, a controller and/or anyother suitable processing device may be used, configured and/orprogrammed to perform the example process of FIG. 3. For example, theprocess of FIG. 3 may be embodied in coded instructions stored on atangible computer-readable medium. Machine-readable instructionscomprise, for example, instructions that cause a processor, a computerand/or a machine having a processor to perform one or more processes.Alternatively, some or all of the example process of FIG. 3 may beimplemented using any combination(s) of ASIC(s), PLD(s), FPLD(s),FPGA(s), fuses, discrete logic, hardware, firmware, etc. Also, some orall of the example process of FIG. 3 may be implemented manually or asany combination of any of the foregoing techniques, for example, anycombination of firmware, software, discrete logic and/or hardware.Further, many other methods of implementing the example operations ofFIG. 3 may be employed. For example, the order of execution of theblocks may be changed, and/or one or more of the blocks described may bechanged, eliminated, sub-divided, or combined. Additionally, the blocksof any or all of the example process of FIG. 3 may be carried outsequentially and/or carried out in parallel by, for example, separateprocessing threads, processors, devices, discrete logic, circuits, etc.

The example process of FIG. 3 begins with the example data manager 210obtaining performance data for a presently considered subscriber loopfrom the aggregate database 155 via the example database interface 205(block 305). Using measured attenuation data, the data manager 210computes or estimates the EWL of the presently considered loop (block310).

The data manager 210 obtains from the aggregate database 155 via thedatabase interface 205 one or more NMR values previously measured forthe presently considered subscriber loop (e.g., one of the examplesubscriber loops 130, 131) (block 315). For each collected NMR value,the data manager 210 uses the timestamp associated with a currentlyconsidered NMR value to obtain associated topology information (block320). As described above in connection with FIG. 2, the data manager 210uses the obtained topology information to compute the disturber termsM(i,25pair), M(i,other75), M(i,other_cable) and M(i,other_DA) (block325).

Using the obtained NMR values, the estimated EWL, and the computeddisturber counts, the example modeler 210 uses a least-squaresregression and/or analysis to determine and/or select the coefficientsof the example NMR model of EQN (1) (block 330). The example performancegain estimator 220 of FIG. 2 uses EQN (1) to compute a first NMR valueassuming disturbers are enabled and a second NMR value assuming thedisturbers are disabled (block 335). The performance gain estimator 220computes a difference between the first and second NMR values thatrepresents an estimate of the performance gain achievable if vectoringwere provisioned for the subscriber loop (block 340).

In some examples, the example performance gain estimator 220 usesShannon's Law to estimate a bit rate and/or an error rate improvementthat may result if the predicted vectored DSL performance gain wasachieved in practice (block 345). Control then exits from the exampleprocess of FIG. 3.

FIG. 4 is a block diagram of an example processor platform P100 capableof executing the example process of FIG. 3 to estimate the vectored DSLperformance gains. The example processor platform P100 can be, forexample, a computer, a workstation, a server and/or any other type ofcomputing device containing a processor.

The processor platform P100 of the instant example includes at least oneprogrammable processor P105. For example, the processor P105 can beimplemented by one or more Intel® microprocessors from the Pentium®family, the Itanium® family or the XScale® family. Of course, otherprocessors from other processor families and/or manufacturers are alsoappropriate. The processor P105 executes coded instructions P110 and/orP112 present in main memory of the processor P105 (e.g., within avolatile memory P115 and/or a non-volatile memory P120) and/or in astorage device P150. The processor P105 may execute, among other things,the example machine-accessible instructions of FIG. 4 to estimate theperformance gain due to vectoring. Thus, the coded instructions P110,P112 may include the example instructions of FIG. 3.

The processor P105 is in communication with the main memory includingthe non-volatile memory P120 and the volatile memory P115, and thestorage device P150 via a bus P125. The volatile memory P115 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of RAM device. The non-volatile memory P120 may beimplemented by flash memory and/or any other desired type of memorydevice. Access to the memory P115 and the memory P120 may be controlledby a memory controller.

The processor platform P100 also includes an interface circuit P130. Anytype of interface standard, such as an external memory interface, serialport, general-purpose input/output, an Ethernet interface, a universalserial bus (USB), and/or a PCI express interface, etc, may implement theinterface circuit P130.

The interface circuit P130 may also include one or more communicationdevice(s) P145 such as a network interface card to facilitate exchangeof data, packets, and/or routing information with other nodes, servers,devices and/or routers of a network.

In some examples, the processor platform P100 also includes one or moremass storage devices P150 to store software and/or data. Examples ofsuch storage devices P150 include a floppy disk drive, a hard diskdrive, a solid-state hard disk drive, a CD drive, a DVD drive and/or anyother solid-state, magnetic and/or optical storage device. The examplestorage devices P150 may be used to, for example, store the examplecoded instructions of FIG. 3 and/or the aggregate database 155 of FIG.1.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all systems,methods, apparatus and articles of manufacture falling within the scopeof the claims of this patent.

1. A method comprising: determining a model coefficient of anoise-to-margin ratio (NMR) model from performance data measured for adigital subscriber line (DSL) subscriber loop prior to provisioning ofvectoring for the DSL subscriber loop; computing, using the modelcoefficient, a first NMR value with disturbers enabled and a second NMRvalue with disturbers disabled; and estimating an expected vectoringperformance gain for the DSL subscriber loop based on the first andsecond NMR values.
 2. A method as defined in claim 1, further comprisingestimating at least one of a bit rate improvement or an error rateimprovement based on the expected vectoring performance gain.
 3. Amethod as defined in claim 1, further comprising using Shannon's law toestimate the at least one of the bit rate improvement or the error rateimprovement.
 4. A method as defined in claim 1, further comprising usinga least squares analysis of the performance data to determine the modelcoefficient.
 5. A method as defined in claim 4, further comprisingcomputing a variable of the least squares analysis, wherein the variableis at least one of an equivalent working length, or a number ofdisturbers.
 6. A method as defined in claim 1, wherein determining themodel coefficient comprises: querying a database to obtain attenuationdata for the DSL subscriber loop; and estimating an equivalent workinglength for the DSL subscriber loop based on the attenuation data; andcomputing a number of disturbers representing a number of active DSLservices in at least one of a cable binder, a cable binder complement,or a distribution area.
 7. A method as defined in claim 6, wherein theequivalent working length is estimated using a curve fitting.
 8. Anapparatus comprising: a modeler to determine a model coefficient using aleast squares analysis of performance data measured for a digitalsubscriber line (DSL) subscriber loop prior to provisioning of vectoringfor the DSL subscriber loop; and a performance gain estimator to:compute, using the model coefficient, a first noise-to-margin ratio(NMR) value with disturbers enabled and a second NMR value withdisturbers disabled; and estimate an expected vectoring performance gainfor the DSL subscriber loop from the first and second NMR values.
 9. Anapparatus as defined in claim 8, further comprising a data manager toestimate an equivalent working length for the DSL subscriber loop basedon attenuation data for the DSL subscriber loop, and to compute a numberof disturbers representing a number of active DSL services in at leastone of a cable binder, a cable binder complement, or a distributionarea.
 10. An apparatus as defined in claim 9, wherein the data manageris to compute an independent variable of the least squares analysis, theindependent variable being at least one of an equivalent working length,or a number of disturbers.
 11. An apparatus as defined in claim 8,wherein the performance gain estimator is to compute at least one of abit rate improvement or an error rate improvement based on the expectedvectoring performance gain.
 12. An apparatus as defined in claim 11,wherein the performance gain estimator is to use Shannon's law toestimate the at least one of the bit rate improvement or the error rateimprovement.
 13. An apparatus as defined in claim 9, wherein theperformance data comprises at least one of an attenuation, or a thirdNMR value.
 14. An article of manufacture storing machine-accessibleinstructions that, when executed, cause a machine to at least: determinea model coefficient of a noise-to-margin ratio (NMR) model fromperformance data measured for a digital subscriber line (DSL) subscriberloop prior to provisioning of vectoring for the DSL subscriber loop;compute, using the model coefficient, a first NMR value with disturbersenabled and a second NMR value with disturbers disabled; and estimate anexpected vectoring performance gain for the DSL subscriber loop based onthe first and second NMR values.
 15. An article of manufacture asdefined in claim 14, wherein the machine-accessible instructions, whenexecuted, cause the machine to estimate at least one of a bit rateimprovement or an error rate improvement based on the expected vectoringperformance gain.
 16. An article of manufacture as defined in claim 15,wherein the machine-accessible instructions, when executed, cause themachine to use Shannon's law to estimate the at least one of the bitrate improvement or the error rate improvement.
 17. An article ofmanufacture as defined in claim 15, wherein the machine-accessibleinstructions, when executed, cause the machine to perform a leastsquares analysis of the performance data to determine the modelcoefficient.
 18. An article of manufacture as defined in claim 17,wherein the machine-accessible instructions, when executed, cause themachine to compute an independent variable of the least squaresanalysis, wherein the independent variable is at least one of anequivalent working length, a third NMR value, or a number of disturbers.19. An article of manufacture as defined in claim 14, wherein themachine-accessible instructions, when executed, cause the machine todetermine the model coefficients by: querying a database to obtainattenuation data for the DSL subscriber loop; and estimating anequivalent working length for the DSL subscriber loop based on theattenuation data; and computing a number of disturbers representing anumber of active DSL services in at least one of a cable binder, a cablebinder complement, or a distribution area.
 20. An article of manufactureas defined in claim 19, wherein the machine-accessible instructions,when executed, cause the machine to estimate the equivalent workinglength using a curve fitting.